scholarly journals Bidding Models Analysis on Ship Repair Projects (Friedman and Ackoff & Sasieni Models)

Tibuana ◽  
2021 ◽  
Vol 4 (02) ◽  
pp. 104-109
Author(s):  
Aditya Maharani ◽  
Fitri Hardiyati ◽  
Ali Subagyo
Keyword(s):  
Normal Distribution ◽  
Statistical Method ◽  
Discrete Distribution ◽  
Price Strategy ◽  
Shipping Industry ◽  
Bid Price ◽  
Ship Repair ◽  
Bidding Models

The existence of a ship project carried out with a tender system by the LPSE allows all shipyard industries to bid on the project, this causes the chances of winning to become smaller, the determination of the tender price greatly determines the size of the profit that can be obtained and the percentage of the possibility of winning the project in a shipping industry. Therefore, the strategy of determining the bid price is very important. The statistical method used is multi discrete distribution, and multi normal distribution, while the bidding model uses Friedman (1956) and Ackoff & Sasieni (1968) models. The results obtained the best bid price strategy to win an auction or tender is the model that produces the lowest optimum mark-up, namely the Friedman model with multi normal distribution, while for Ackoff & Sasieni it produces a higher bid than the Friedman model except in certain company conditions.

Statistical method of steepest improvement of response

2018 ◽  
Vol 84 (11) ◽  
pp. 74-87
Author(s):  
V. B. Bokov
Keyword(s):  
Statistical Method ◽  
Linear Model ◽  
Response Prediction ◽  
Initial Experiment ◽  
First Order ◽  
Prediction Response ◽  
Conditional Extremum ◽  
Order Polynomial ◽  
Limiting Values

A new statistical method for response steepest improvement is proposed. This method is based on an initial experiment performed on two-level factorial design and first-order statistical linear model with coded numerical factors and response variables. The factors for the runs of response steepest improvement are estimated from the data of initial experiment and determination of the conditional extremum. Confidence intervals are determined for those factors. The first-order polynomial response function fitted to the data of the initial experiment makes it possible to predict the response of the runs for response steepest improvement. The linear model of the response prediction, as well as the results of the estimation of the parameters of the linear model for the initial experiment and factors for the experiments of the steepest improvement of the response, are used when finding prediction response intervals in these experiments. Kknowledge of the prediction response intervals in the runs of steepest improvement of the response makes it possible to detect the results beyond their limits and to find the limiting values of the factors for which further runs of response steepest improvement become ineffective and a new initial experiment must be carried out.

scholarly journals Calculation of nuclear energies and stability by the statistical method

1940 ◽  
Vol 174 (959) ◽  
pp. 523-545 ◽  
Keyword(s):  
Statistical Theory ◽  
Binding Energy ◽  
Statistical Method ◽  
Atomic Number ◽  
Quantum States ◽  
Hartree Approximation ◽  
Stable Nucleus ◽  
The Stability

Leaving out of consideration those nuclei of small atomic number it is possible to develop a statistical theory of nuclei. Bethe and Bacher (1936, p. 149), as well as many other writers, have treated this subject in great detail starting from the Hartree approximation. All these investigations were mainly concerned with the binding energy, and not much attention has been paid so far to the stability of nuclei according to the statistical theory, except the determination of the most stable nucleus with a given atomic number: this is due to the fact that previous investigators have always neglected to distinguish between quantum states with opposite spin, thereby losing the distinction between “odd” and “even” nuclei, which is essential for stability considerations.

Determination of the correlation relationship between irrigation and distribution of agricultural lands

2021 ◽  
Vol 07 (02) ◽  
pp. 29-35
Author(s):  
Kamil Zakir oğlu İbrahimli ◽  
Keyword(s):  
Key Words ◽  
Statistical Method ◽  
Negative Correlation ◽  
Agricultural Lands ◽  
Correlation Relationship ◽  
Positive Correlation ◽  
Correlation Methods ◽  
Correlation Degree ◽  
The Relationship

The importance of the statistical method is great in the study of natural and economic processes. It is possible to conduct analyzes and obtain results with the help of this method. Sometimes phenomena and processes co-exist and develop interdependently. In this case, it is necessary to determine the relationship between them, to express this relationship. Key words: perfect positive correlation, perfect negative correlation, direction of correlation, degree of correlation, methods of calculating correlation, Spearman᾽s method

Determination of a most representative cycle from cylinder pressure ensembles via statistical method using distribution skewness

2021 ◽  
pp. 146808742110655
Author(s):  
Jorge Pulpeiro González ◽  
Carrie M Hall ◽  
Christopher P Kolodziej
Keyword(s):  
Statistical Method ◽  
Combustion Engine ◽  
Pressure Rise ◽  
Operating Conditions ◽  
Cylinder Pressure ◽  
Pressure Trace ◽  
Combustion Knock ◽  
Engine Operating Condition ◽  
Combustion Processes

In internal combustion engine research, cylinder pressure measurements provide valuable information about the underlying thermodynamic and combustion processes, and are typically collected in ensembles of several 100 traces. Although in some particular fields of combustion research all traces are analyzed, in most cases only one trace is studied because analyzing all the traces is impractical due to the large number of collected samples. Instead, an ensemble-averaged pressure trace is commonly calculated and used for analysis. However, this pressure trace is highly smoothed and dynamic information is lost during the averaging process. With the average trace, pressure rise rates are lower and pressure oscillations such as the ones resulting from combustion knock are lost. In this work, a statistical method was developed to determine the “most representative cycle,” which is the cycle from the ensemble that has the pressure trace most representative of the engine operating condition. Eleven characteristic parameters are computed from each pressure trace and probabilistic distributions are obtained for each of the parameters using all the traces in the ensemble. Finally, the most representative cycle is selected by means of a cost function minimization. The benefits of this method are illustrated using experimental data from four very different engine platforms, under four different combustion modes and over a range of operating conditions.

A New Statistical Method for the Determination of Dynamical Features of Molecular Dication Dissociation Processes

2012 ◽  
pp. 432-446 ◽  
Author(s):  
Maria Suely Pedrosa Mundim ◽  
Pietro Candori ◽  
Stefano Falcinelli ◽  
Kleber Carlos Mundim ◽  
Fernando Pirani ◽  
...  
Keyword(s):  
Statistical Method ◽  
Dynamical Features

Probability Distribution of Tidal Elevation Data around Korean Coasts

2013 ◽  
Vol 284-287 ◽  
pp. 1484-1488
Author(s):  
Hong Yeon Cho ◽  
Shin Taek Jeong ◽  
Dong Hui Ko ◽  
Sang Ho Lee
Keyword(s):  
Normal Distribution ◽  
Density Function ◽  
Optimal Parameter ◽  
Kernel Functions ◽  
Normal Distribution Function ◽  
Tidal Elevation ◽  
Significance Level ◽  
Normality Test ◽  
Elevation Data

Frequency information of tidal elevations in the coastal zone is essential for the determination of datum level, the classification of inhabitation zones, and the analysis of mean sea level variation. In this study, the non-parametric density function is suggested for the analysis of hourly tidal elevation data provided by the Korea Hydrographic and Oceanographic Administration. The density function was estimated for six principal locations, Incheon, Mokpo, Jeju, Yeosu, Busan, and Pohang in the Korean coastal area using a kernel function. The parameter required for the probability density function was optimally estimated with the Sheather and Jones (SJ). And the optimal parameter appropriate for the normal distribution function was about 30% higher than that predicted by the SJ method or the Cross Validation (CV) method. It can be seen that the final kernel functions were less affected. The smoothing parameters for all of the tidal elevation data were optimized to be in the range of 0.13-0.17 with the SJ method. From the normality test of the observed tidal elevation data, it was proposed that the hypothesis of a normal distribution was inappropriate in the test techniques with a 95% significance level.

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